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To assess the utility of SpaMTP, we analyzed multiple biological systems, including human lung8, murine urinary bladder9, murine brain7 and human glioma10, where the two latter contained paired ST–SM data at spot and single-cell resolution. Using the human lung dataset, we first compared SpaMTP’s metabolite annotation protocol to previously published METASPACE results (Fig. 2a and Methods). With a ±3-ppm threshold, SpaMTP annotated 80,235 and 39,215 m/z values from HMDB and LipidMaps, respectively, exceeding METASPACE (247 and 322 annotations at 20% false discovery rate). Among overlapping annotations, ~90% (HMDB: 87%, LipidMaps: 93%) matched, with many mismatches attributable to synonyms (Supplementary Table 1). Analyzing a spiked-in dataset, SpaMTP showed more accurate annotation than METASPACE (Extended Data Fig. 1). This highlights SpaMTP’s accuracy and usability while offering convenient offline implementation. Of note, SpaMTP implements four additional strategies to fine-tune annotation (Methods). SpaMTP downstream analyses To further demonstrate SpaMTP’s SM analysis capabilities, we next analyzed a mouse urinary bladder dataset9 (Fig. 2b). SpaMTP-assigned annotations for m/z values correctly matched those MS/MS-validated annotations in the original publication (Supplementary Table 2). Assessing the interoperability of SpaMTP, we converted the SpaMTP object to a Cardinal class to run Cardinal metabolite-only clustering. The resulting clusters aligned to defined...